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ajitjohnson / imsig

Immune Cell Gene Signatures for Profiling the Microenvironment of Solid Tumours
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error in running the example_data #1

Closed natlasy closed 6 years ago

natlasy commented 6 years ago

Hi,

I'm getting an error in running the 'imsig' package as below:

cell_abundance = imsig(exp = example_data, r = 0.7) Error in sig[sig$gene %in% as.character(g), ] : incorrect number of dimensions

The example_data looks fine though:

dim(example_data) [1] 568 60 head(example_data, n=6) GSM512479.CEL GSM512480.CEL GSM512481.CEL GSM512482.CEL GSM512483.CEL AFF3 342.4006 52.88693 78.28706 101.1696 43.63739 BANK1 2535.4058 280.72550 498.01262 390.2110 697.87245 BLK 814.3128 132.36280 304.39403 304.8484 264.01830 BTLA 391.7088 152.33024 119.84629 202.0018 255.36544 CCR6 537.1195 153.71538 154.45685 254.8858 175.81826 CD180 821.2340 296.36957 234.71300 273.3661 353.07089

Any idea?

Thanks, natlasy

ajitjohnson commented 6 years ago

Hi Natlasy, Sorry that you are having this issue. I am not able to reproduce your error. This works perfectly for me:

library("imsig") cell_abundance = imsig(exp = example_data, r = 0.7) Did you install the package from CRAN, like- install.packages("imsig")?

natlasy commented 6 years ago

Thanks for your quick reply.

Yes. I installed the package from CRAN with no error as below:

install.packages("imsig") Installing package into ‘/home/natlasy/R/x86_64-pc-linux-gnu-library/3.3’ (as ‘lib’ is unspecified) trying URL 'https://ftp.gwdg.de/pub/misc/cran/src/contrib/imsig_1.0.0.tar.gz' Content type 'application/octet-stream' length 265039 bytes (258 KB)

downloaded 258 KB

The downloaded source packages are in ‘/tmp/RtmpwsAUca/downloaded_packages’

My R version is as below:

R.version _
platform x86_64-pc-linux-gnu
arch x86_64
os linux-gnu
system x86_64, linux-gnu
status
major 3
minor 3.1
year 2016
month 06
day 21
svn rev 70800
language R
version.string R version 3.3.1 (2016-06-21) nickname Bug in Your Hair

Could it be an incompatibility with R version issue?

Best, natlasy

ajitjohnson commented 6 years ago

Hi Natlasy, The version does not seem to be an issue. I downloaded R3.3.1 and tested. It works fine (See below). I think it might be something specific to your system. Can you try it on a different system? Although I tested it now on a windows machine, I have had previously tested the package on a mac and it works. Let me know if it works. Thanks.

library(imsig) cell_abundance = imsig(exp = example_data, r = 0.7) head(cell_abundance)[1:3] B cells Interferon Macrophages GSM512532.CEL 102.56179 1230.431 703.5092 GSM512506.CEL 154.47258 2671.506 1095.1257 GSM512524.CEL 101.89446 1723.283 857.7853 GSM512521.CEL 109.79391 1981.511 948.1081 GSM512519.CEL 128.61854 2044.586 1038.1472 GSM512534.CEL 94.22661 1576.260 994.5554

version
platform x86_64-w64-mingw32
arch x86_64
os mingw32
system x86_64, mingw32
status
major 3
minor 3.1
year 2016
month 06
day 21
svn rev 70800
language R
version.string R version 3.3.1 (2016-06-21) nickname Bug in Your Hair

natlasy commented 6 years ago

All right. I tried it on another system with newer version of R and it worked. Then I just got back to the original system and closed the current running session of old R which I first did install the package on it and opened a new session and now it works !! So seemed I had to close/re-open the R.

But when I applied it on my dataset, it is giving me the below error:

cell_abundance = imsig(exp = data, r = 0.7) ---> Maximum number of splits: floor(n/2) = 0 ---> WARNING: number of splits nSplit > 0 ---> WARNING: using maximum number of splits: nSplit = 0 Error in fastCor(t(exp)) : invalid nSplit: 0

My dataset is like this:

dim(data) [1] 4373 2655

data[1:5,1:4] JCD10F36.PL2.Cell.370 JCD10F36.PL2.Cell.205 JCD10F36.PL2.Cell.374 DPM1 0 0 0 FUCA2 0 0 0 GCLC 0 0 0 ENPP4 0 0 0 ANKIB1 0 0 0 JCD10F36.PL2.Cell.375 DPM1 0.000000 FUCA2 0.000000 GCLC 0.000000 ENPP4 0.000000 ANKIB1 1.929464

My dataset is based on single-cell RNAseq experiment ; not sure if this is problematic as we have lots of zero's and the expression levels are mostly 1 or around that.

I also noticed that in the 'example_data' test run, the output gives only 10 cell types; Is this the max of cell types one can get or the package covers all known cell types but gives output assignment based on the dataset?

Thanks a lot for your help!

Best, natlasy

ajitjohnson commented 6 years ago

Hi Natlasy, That's great. I'm sorry this package is not for single-cell RNA Seq data. This package is for deconvoluting bulk transcriptomics data. I presume you are trying to identify cell types in your data. I would recommend using packages like Seurat or Scanpy. Not sure if there is an automated method available.

natlasy commented 6 years ago

All right. I see. Thanks indeed for your follow up and help.

Cheers, natlasy

ajitjohnson commented 6 years ago

No Problem. All the best.